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Search results for machine learning autoencoder
autoencoder
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machine-learning
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93 search results found
Pyod
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7,751
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
Pytorch Tutorial
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7,372
Build your neural network easy and fast, 莫烦Python中文教学
Tensorflow Book
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4,443
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Tensorflow Tutorial
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3,873
Tensorflow tutorial from basic to hard
Alae
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2,850
[CVPR2020] Adversarial Latent Autoencoders
Aialpha
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1,648
Use unsupervised and supervised learning to predict stocks
Niftynet
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1,170
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
Tensorflow 101
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987
TensorFlow 101: Introduction to Deep Learning
Deepsvg
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829
[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.
Tensorflow Tutorial
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751
TensorFlow and Deep Learning Tutorials
Keras Idiomatic Programmer
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705
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework
Handson Unsupervised Learning
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604
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
Torchlayers
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547
Shape and dimension inference (Keras-like) for PyTorch layers and neural networks
Generative Models
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414
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Handson Ml
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277
도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Artificio
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269
Deep Learning Computer Vision Algorithms for Real-World Use
Deep Sad Pytorch
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268
A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.
Deep Learning For Hackers
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196
Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)
Danmf
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187
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
Practical Machine Learning With Python
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182
Machine Learning Tutorials in Python
Numalogic
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156
Collection of operational time series ML models and tools
Kitnet Py
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124
KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.
Gon
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123
Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder
Visualml
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106
Interactive Visual Machine Learning Demos.
Gpnd
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106
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Mlwithtensorflow2ed
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101
Code for Machine Learning with TensorFlow: 2nd Edition Published by Manning Publications
Smrt
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94
Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
Shapegan
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85
Generative Adversarial Networks and Autoencoders for 3D Shapes
Concrete Autoencoders
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83
Cade
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81
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Face Landmarking
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72
Real time face landmarking using decision trees and NN autoencoders
Contiguous Succotash
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70
Recurrent Variational Autoencoder with Dilated Convolutions that generates sequential data implemented in pytorch
Hands On Ml
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69
Hands-On Machine Learning
Video_generator
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63
This is implementation of convolutional variational autoencoder in TensorFlow library and it will be used for video generation.
Wsae Lstm
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61
implementation of WSAE-LSTM model as defined by Bao, Yue, Rao (2017)
Transforming Autoencoder Tf
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59
Tensorflow implementation of "Transforming Autoencoders" (Proposed by G.E.Hinton, et al.)
Recoder
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48
Large scale training of factorization models for Collaborative Filtering with PyTorch
Vae_protein_function
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44
Protein function prediction using a variational autoencoder
Datadrivendynsyst
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40
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
Dcase2020_task2_baseline
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37
DCASE2020 Challenge Task 2 baseline system
Mirapy
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37
MiraPy: A Python package for Deep Learning in Astronomy
Pytorch_integrated_cell
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36
Integrated Cell project implemented in pytorch
Tensorflow_whatwhereautoencoder
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34
Stacked What-Where Auto-encoders implementation wiht Tensorflow
Numpy Nn Model
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32
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
Thio
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31
Thio - a playground for real-time anomaly detection
Vae Anomaly Detector
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29
Experiments on unsupervised anomaly detection using variational autoencoder. The variational autoencoder is implemented in Pytorch.
Zeta Learn
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27
zeta-lean: minimalistic python machine learning library built on top of numpy and matplotlib
Sldm4 H2o
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27
Statistical Learning & Data Mining IV - H2O Presenation & Tutorial
Stock2vec
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26
Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history
Fc
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26
Face enhancer - Denoising Auto Encoder by Tensorflow and Keras and skimage
Deep Learning Visuals
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24
A collection of 100 Deep Learning images and visualizations
Encodermap
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24
python library for dimensionality reduction
Chemotools
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24
Integrate your chemometric tools with the scikit-learn API 🧪 🤖
Recsys_autoencoders
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23
This project implements different Deep Autoencoder for Collaborative Filtering for Recommendation Systems in Keras
Tensorflow_deep_learning_models
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23
TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)
Deeplearningexamples
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22
Deep learning examples with Python and Tensorflow & Keras.
Vde_metadynamics
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20
Enhanced protein mutational sampling using time-lagged variational autoencoders
Deep Steg
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20
Global NIPS Paper Implementation Challenge of "Hiding Images in Plain Sight: Deep Steganography"
Fault Detection For Predictive Maintenance In Industry 4.0
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19
This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.
Mnist Vae
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18
Semi-supervised learning with mnist using variational autoencoders. An unsupervised representation is learned which allows for superior classification results with limited labels.
Variationalautoencoders
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17
Comparison of Variational Autoencoders with Bayesian Neural Networks. Accuracy, Latent space, Reconstruction and White Noise filtering.
Nabnet
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17
NABNet: A Nested Attention-guided BiConvLSTM Network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG Signals
Breast Cancer Sub Types
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17
A novel self-supervised feature extraction method using omics data is proposed which improves classification in most of the classifiers.
Autoencoder_vs_metriclearning
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17
AutoEncoder vs Metric Learning for Anomaly Detection
Min2net
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17
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
Simple Variational Autoencoder
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16
A VAE written entirely in Numpy/Cupy
Introduction To Deep Learning And Neural Networks Course
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16
Code snippets and solutions for the Introduction to Deep Learning and Neural Networks Course hosted in educative.io
Grae
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14
Geometry Regularized Autoencoders (GRAE) for large-scale visualization and manifold learning
Npglm
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14
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks (TKDD 2019)
Sketchcolorization
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12
line drawing colorization using pytorch
Learningtospotartifacts
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12
Self-Supervised Feature Learning by Learning to Spot Artifacts. In CVPR, 2018.
Ml Image Denoising
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12
Image denoising using PCA, NMF, K-SVD, Spectral decomposition, CNN and state of the art generative adversarial denoising autoencoder
Deep Learning Super Resolution Image Reconstruction Dsir
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12
Deep-Learning convolutional auto-encoders applied to super-resolution microscopy data to localization image reconstruction.
Generic Expression Patterns
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11
Distinguishing between generic and experiment-specific gene expression signals.
Feature Selection Techniques
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11
Python code source for features selection 👨🔬 series on medium website. 📰
Wprautoencoders
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11
This is one of Petrobras' open repositories on GitHub. It contains the WPRAutoencoders project which encompasses a wellbore pressure response generator, a dataset of 20.000 synthetic pressure responses and an autoencoder neural network capable of clustering this data based on transmissibility and reservoir geometry.
Quadricloss
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11
Learning Embedding of 3D models with Quadric Loss
Deep Learning Scratch Arena
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10
Implementing most important basic building blocks of Deep Learning from scratch.
Manifold Linearization
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10
Companion repository for the paper "Representation Learning via Manifold Flattening and Reconstruction"
Label Free Xai
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10
This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.
Ml_wirelesscomm
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10
Machine Learning Applications in Wireless Communications - Project work
Tss18 Robotsinmusicalimprovisation
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9
Robots in Musical Improvisation: Learning Algorithms Respecting Motion Constraints
Glaucus
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9
Glaucus is a PyTorch complex-valued ML autoencoder & RF estimation python module.
Lanternfish
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9
Deep convolutional neural networks for biological motion analysis
Hybrid Recommender
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9
Hybrid recommendation engine using deep learning that incorporates user and item features, including images and text.
Network Intrusion Detection Using Machine Learning
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8
A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach
Machine_learning
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8
Best collection of machine learning & deep learning algorithms implemented from scratch using python.
Osraae
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8
Open-set Recognition with Adversarial Autoencoders
Self Supervised Bss Via Multi Encoder Ae
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8
Official repository for "Self-Supervised Blind Source Separation via Multi-Encoder Autoencoders".
Ml2018spring
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8
NTUEE 2018 spring course - Machine Learning (Pei-Yuan Wu, Hung-Yi Lee, Tsungnan Lin)
Denoise_autoencoder
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8
The implement of layer-wise training denoise autoencoder in pytorch.
Phase Of Matter By Machine Learning
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8
Unsupervised machine learning of phase of matter in physics
Dcase2021_task2_baseline_ae
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8
Autoencoder-based baseline system for DCASE2021 Challenge Task 2.
Autoembedder
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7
PyTorch autoencoder with additional embeddings layer for categorical data 🚘
Spectral_metric_for_dataset_complexity_assessment Keras
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7
Implementation for Spectral Metric for Dataset Complexity Assessment(CVPR2019)
Multicolor Shapes Database
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7
A small database to test different machine learning tasks. It contains simple shapes of different colors.
Autoencoder
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7
Simple Implementation of AutoEncoder, one type of deep learning algorithm. This is implemented based on Tensorflow.
Tensorflow2.0_notebooks
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6
Implementation of a series of Neural Network architectures in TensorFow 2.0
Oneclassclassifier
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6
One-class classifiers for anomaly detection (outlier detection)
Deeplearning Lookup
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6
ML/DL Lookup Table / 機械学習・深層学習の単語集
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